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The global Industry 4.0 market was worth $116 billion in 2021 and is predicted to rise to $337 billion by 2028. Many technologies are contributing to the incredible growth of Industry 4.0, but a standout among them is digital twin solutions. Specifically, digital twins are now being deployed to greatly improve warehouse automation operations with the end goal of increasing efficiency and reducing downtime. 

Digital twins can deliver virtual representations of a physical environment — proving extremely helpful to the warehouse industry. With a digital twin, new improvements and efficiencies can be tested virtually, without downtime or rearrangement of physical assets.

Warehouse operations are rapidly growing in complexity. Inventory is more diverse, as the massive expansion of ecommerce has brought an increase in the proliferation of SKUs. Logistics solutions are strained, as customers now expect lightning-fast fulfillment. Technology is more complex, as innovative new automation systems come to market, and managers must analyze the new systems to introduce those that bring the greatest benefit to their warehouse operations.

To win against competitors, smart companies are now building digital twins of their warehouse operations and using them to handle operational complexities and performance improvements. 


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Visualization and design made easier

With digital twins, companies can try out new floor plans and test new workflows virtually. They can introduce new variables and configuration parameters to the virtual model of their operations and assess the impact. Every aspect of operations can be monitored and tweaked, including SKU mix, ordering and shipping and demand spikes. Automation efforts can be tested and their impact reviewed. In short, warehouse performance can be improved far more quickly and cost-effectively than in the past.

Digital twins are also valuable in the design of new automation systems. For a traditional automation system, thousands of hours are invested in configuring and adjusting the software that runs the system. One size does not fit all. For each system, each customer’s different materials and processes must be accommodated. But what if it were possible to skip all the coding of these customer specifics? It is, with a digital twin of the physical system, in which a machine learning algorithm can run experiments. It usually takes up to a year to create and install a system; but in that time, machine-learning can run its experiments and the physical system can be optimal on the day it goes live.

Digital twins can also help businesses adapt to changing requirements or other disruptions. How far can you push your operation during peak holiday shopping periods? What happens if customer buying patterns change? How will your system respond to equipment downtime? A warehouse or distribution center empowered with digital twin technology can run hundreds of possible scenarios in minimal time. This provides critical insights of average performance over time and identifies areas where you can bolster your system’s ability to handle short- and long-term challenges. It also enables much more timely decisions about when to service, upgrade or replace the system.

Robotic systems go digital

The digitization of robotic systems continues to enable next-generation automation within the warehouse space. The synthetic models that feed intelligence to the algorithm train the internal dataset to replicate into a digital twin, showing the end user the best-case scenarios of performance. Augmenting these processes gives the robotic systems the optimal path for project completion. In previous applications, these technological processes equated to months of implementation in the warehouse. With the digital twin data, these processes are now reduced to weeks. Increased investment and R&D in machine learning will continue to streamline operations for better efficiency and reduced downtime.

Forward-thinking companies are now using digital twins as a key tool in their digital transformation. Their operations and maintenance teams are gaining valuable new insights from digital twins to make more timely and relevant decisions. They’re synchronizing digital twins with their real-world environments to analyze the performance of their current processes and improve them. With integrated process and asset data, combined with predictive analytics, they’re reaching new levels of productivity and better identifying any looming issues. They’re boosting performance, efficiency, yield and uptime.

There is now an intense focus on digital transformation initiatives and industrial IoT — and digital twins are playing a vital part. They’re greatly improving performance and ROI by enabling far more efficient, rapid and cost-effective analysis and decision-making. 

Thomas H. Evans, Ph.D. is CTO of robotics at Honeywell.


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